Data-Driven Energy Estimation for Virtual Servers Using Combined System Metrics and Machine Learning

Amandip Sangha
9/12/2025
cs.LG

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Abstract

This paper presents a machine learning-based approach to estimate the energy consumption of virtual servers without access to physical power measurement interfaces. Using resource utilization metrics collected from guest virtual machines, we train a Gradient Boosting Regressor to predict energy consumption measured via RAPL on the host. We demonstrate, for the first time, guest-only resource-based energy estimation without privileged host access with experiments across diverse workloads, achieving high predictive accuracy and variance explained ($0.90 \leq R^2 \leq 0.97$), indicating the feasibility of guest-side energy estimation. This approach can enable energy-aware scheduling, cost optimization and physical host independent energy estimates in virtualized environments. Our approach addresses a critical gap in virtualized environments (e.g. cloud) where direct energy measurement is infeasible.

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Cite this paper

@article{sangha2025datadriven,
  title  = {Data-Driven Energy Estimation for Virtual Servers Using Combined System Metrics and Machine Learning},
  author = {Amandip Sangha},
  year   = {2025},
  eprint = {2509.09991},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.09991v1}
}

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